Article(id=1309639317216134045, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730649600000, receivedDateStr=2024-11-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171945145, onlineDateStr=2026-09-23, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171945145, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171945145, creator=13701087609, updateTime=1790171945145, updator=13701087609, issue=Issue{id=1309639316763149213, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='12', pageStart='1563', pageEnd='1704', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171945038, creator='13701087609', updateTime=1790231727008, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890060590346990, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890060594541295, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1588, endPage=1597, ext={EN=ArticleExt(id=1309639317396489118, articleId=1309639317216134045, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Modeling method for photovoltaic arrays based on implicit double-diode model and reverse bias model, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the limitations of traditional photovoltaic (PV) array modeling methods in terms of computational accuracy and portability, this study proposes a novel PV array modeling approach based on the Implicit Double Diode Model (IDDM) and the Reverse Bias Model (RBM). The proposed method first configures environmental parameters according to the actual operating conditions of the PV array. Subsequently, it calculates the current and voltage characteristics of PV cells under various operating states based on the theoretical frameworks of IDDM and RBM. Following this, the method determines the current and voltage characteristics of PV array substrings and finally outputs the current-voltage (I-V) characteristics of the PV array. Experimental results demonstrate that the proposed method achieves high simulation accuracy, with a theoretical-to-actual current sequence error of only 0.015 A. Additionally, the model effectively simulates the I-V characteristics of PV arrays under scenarios such as partial shading, short circuits, and degradation of equivalent series and parallel resistances. This modeling approach provides a robust theoretical foundation and data support for PV power plant fault diagnosis and analysis.

, authors=Haifei Wu¹, Kun Ding², Ying Su¹, Xiang Chen², Yaoxi He³, Fangxu Ji³, Quanru Chen², authorsList=Haifei Wu, Kun Ding, Ying Su, Xiang Chen, Yaoxi He, Fangxu Ji, Quanru Chen, authorCompany=null, correspAuthors=Kun Ding, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639317602010015, articleId=1309639317216134045, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于隐式双二极管模型和反偏模型的光伏阵列建模方法, columnId=null, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

为解决传统光伏阵列建模方法在计算精度与可移植性上的不足，文章提出一种基于隐式双二极管模型（Implicit Double Diode Model, IDDM）和反偏模型（Reverse Bias Model, RBM）的光伏阵列建模方法。该方法首先根据光伏阵列实际运行状态配置环境参数；其次，基于IDDM与RBM的理论模型，计算不同运行状态下光伏电池的电流电压；再次，计算光伏阵列子串的电流与电压；最后，输出光伏阵列的I-V（Current-Voltage, I-V）特性曲线。实验结果表明，该建模方法具有较高仿真精度，理论与实际电流序列误差可减小到0.015 A。另外，该方法能够有效模拟光伏阵列在局部阴影遮挡、短路、等效串联电阻和并联电阻老化等状态下的I-V特性，可为光伏电站故障诊断研究提供理论依据与数据支撑。

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